Interactive assistance systems typically provide feedback after an action has been completed, supporting error recovery but not preventing the error itself. We present TRAFA, a real-time predictive feedback system for procedural tasks that intervenes before errors are committed. TRAFA operationalizes predictive feedback through a Track-Forecast-Act framework that tracks hand and object state, forecasts user motion conditioned on scene context, and triggers feedback when a predicted action is likely to violate task constraints. We instantiate this pipeline in a sequential assembly setting and evaluate it through both technical benchmarking and a controlled user study against conventional reactive feedback. Our results show that predictive feedback improves task accuracy and efficiency while maintaining a comparable number of feedback events. These findings position feedback timing as a key dimension in system design and show how real-time anticipation can be integrated into interactive systems to prevent errors before they occur.

翻译：交互式辅助系统通常在动作完成后提供反馈，支持错误恢复但无法预防错误本身。我们提出TRAFA，一种针对程序性任务的实时预测性反馈系统，可在错误发生前进行干预。TRAFA通过"跟踪-预测-行动"框架实现预测性反馈：跟踪手部与物体状态，基于场景上下文预测用户运动，并在预测动作可能违反任务约束时触发反馈。我们在顺序装配场景中实例化该流程，并通过技术基准测试和对照用户研究（与常规反应式反馈对比）对其进行评估。结果表明，预测性反馈在保持反馈事件数量相当的同时，提升了任务准确性与效率。这些发现将反馈时机定位为系统设计的关键维度，并展示了如何将实时预判能力整合至交互系统以在错误发生前阻止其产生。